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Abstract:
Improving knowledge of karst hydrodynamics represents a global challenge for water resources because karst aquifers provide approximately 25% of the world population in fresh water. Nevertheless, complexity, anisotropy, heterogeneity, non-linearity and possible non-stationarity of these aquifers make them underexploited objects due to the difficulty to characterize their morphology and hydrodynamics. In this context, the systemic paradigm proposes others methods by studying these hydrosystems through input-output (rainfall-runoff) relations.

The approach proposed in this thesis is to use information from field measurement and from systemic analyses to constrain neural network models. The goal is to make these models interpretable in terms of hydrodynamic processes by making model functioning to be similar to natural system in order to obtain a good representation and extract knowledge from model parameters.

This work covers the association of information available on the hydrosystem with correlation and spectral analyses to develop a temporal multiresolution decomposition of variables and to constrain neural network models. A new method for variable selection, adapted to represent long term hydrodynamics of the system, has been proposed. These constrained models show very good results and allow, through their parameters, to study the temporal contribution of inputs variables to the output.

Modeling nonlinear and non-stationary hydrosystems with neural network has been improved by a novel implementation of data assimilation. More precisely, when non-stationarity is attributed to the catchment, data assimilation is used to modify the model parameters. When the inputs are non-stationary, data assimilation can be used to modify the inputs.

The modification of inputs opens considerable scope to: i) fill gaps or homogenizing time series, ii) estimate effective rainfall.

Finally, these various analyses and modeling methods, mainly developed on the karst hydrosystem Lez, can improve the knowledge of the rainfall-runoff relationship at different time scales. These methodological tools thus offer perspectives of better management of the aquifer in terms of floods and resources. The advantage of these analyses and modeling tools is that they can be applicable to other systems.